Deep Learning: GANs and Variational Autoencoders (Udemy.com)

Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow

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Created by: Lazy Programmer Inc.

Produced in 2021

What you will learn

Learn the basic principles of generative models
Build a variational autoencoder in Theano and Tensorflow
Build a GAN (Generative Adversarial Network) in Theano and Tensorflow

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Course Description

Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently.
Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs.
GAN stands for generative adversarial network, where 2 neural networkscompete with each other.
What is unsupervised learning?
Unsupervised learning means were not trying to map input data to targets, were just trying to learn the structure of that input data.
Once weve learned that structure, we can do some pretty cool things.
One example is generating poetry - weve done examples of this in the past.
But poetry is a very specific thing, how about writing in general?
If we can learn the structure of language, we can generate any kind of text. In fact, big companies are putting in lots of money to research how the news can be written by machines.
But what if we go back to poetry and take away the words?
Well then we get art, in general.
By learning the structure of art, we can create more art.
How about art as sound?
If we learn the structure of music, we can create new music.
Imagine the top 40 hits you hear on the radio are songs written by robots rather than humans.
The possibilities are endless!
You might be wondering, "how is this course different from the first unsupervised deep learning course?"In this first course, we still tried to learn the structure of data, but the reasons were different.
We wanted to learn the structure of data in order to improve supervised training, which we demonstrated was possible.
In this new course, we want to learn the structure of data in order to produce more stuff that resembles the original data.
This by itself is really cool, but we'll also be incorporating ideas from Bayesian Machine Learning, Reinforcement Learning, and Game Theory. That makes it even cooler!
Thanks for reading and Ill see you in class. =)"If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:
CalculusProbabilityObject-oriented programmingPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operationsLinear regressionGradient descentKnow how to build a feedforward andconvolutionalneural network in Theano or TensorFlowWHATORDERSHOULDITAKEYOURCOURSESIN?:
Check out the lecture "Machine Learning and AIPrerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)Who this course is for:
Anyone who wants to improve their deep learning knowledge

Time: 8h

Instructor Details

Lazy Programmer Inc.

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Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.
I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.
Experience includesonline advertising and digital media as both a data scientist (optimizing click and conversion rates)and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop,Pig, Hive,MapReduce, and Spark.
I've created deeplearning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.
My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and wevalidated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.
Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postg